POS0257 PAINWAVES: THE POTENTIAL OF MACHINE LEARNING TO DIFFERENTIATE CHRONIC PAIN COHORTS USING ELECTROENCEPHALOGRAPHY
Authors: J. Tsigarides, A. Rushbrooke, A. Bagnall
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Chronic pain is highly prevalent, debilitating and lacks effective treatments. Experimental and neuroimaging research demonstrates abnormal central pain processing<sup>[1]</sup>. However, robust brain-based biomarkers that could inform targeted treatments are lacking. Electroencephalography (EEG) is the optimal tool to investigate dynamic abnormalities in pain processing to reveal underlying mechanisms. Early evidence from EEG studies in Fibromyalgia (FM) indicate potential mechanisms such as thalamocortical dysrhythmia<sup>[2]</sup>, demonstrated by alterations in the Alpha and Theta frequency bands. However, existing studies employ rudimentary analyses failing to account for the multivariate and temporal nature of EEG data. State-of-the-art machine learning (ML) approaches provide unique opportunities to generate a deeper understanding of EEG signatures in chronic pain and identify specific biomarkers that could be used to differentiate mechanistic subtypes. <h3>Objectives</h3> This preliminary work aims to establish whether a state-of-the-art ML classifier can differentiate patients with FM from healthy controls based on their EEG characteristics. <h3>Methods</h3> The dataset used was collected through The VIPA Study (ISRCTN46681140). Patients with FM satisfied the 2016 FM classification criteria. High-density 64-channel EEG data using an electrolyte gel-based active electrode system was collected at rest with participants' eyes closed over 2 minutes. Individual Fast Fourier Transforms were applied to overlapping time windows to extract EEG frequency band power whilst retaining temporal information. Frequency bands were used as features to train a classification model (definitions of frequency ranges in Table 1). One of the fastest, most accurate state-of-the-art time-series classification ML algorithms (mini-ROCKET) was used via the sktime python toolkit. To obtain unbiased accuracy estimates across all participants, a 'leave-one-out' strategy was used. Accuracy of the algorithm was reported; defined by the number of correct predictions divided by the total number of predictions for each frequency band (2-class problem, chance estimates 0.5-0.6 based on p&lt;0.05). <h3>Results</h3> Data from 23 patients with FM (mean age 46 ±14yrs, 87% female) and 14 healthy controls (mean age 71 ±7yrs, 50% female) were analysed. Patients with FM had moderate self-reported pain (5.5 ±2.3 VAS) and disease severity (mean FIQR 65.4 ±16.6) at baseline. Theta and Alpha were the most discriminatory frequency bands, with mini-ROCKET able to classify participants with 70.7% accuracy. The Beta frequency band was less discriminatory (63.4%) but still above predicted chance estimates. Delta frequency band was least discriminatory (51.2%). <h3>Conclusion</h3> Preliminary results indicate that machine learning can be successfully used to differentiate patients with Fibromyalgia from healthy controls based on EEG measures of the Alpha and Theta frequency bands. Alterations in Alpha and Theta have been demonstrated in previous non-ML research, indicating potential underlying abnormalities in the interaction between the thalamus and cortex which may be related to central mechanisms underlying chronic pain. Further work is required in larger, matched cohorts to validate these findings, but this early work highlights the future potential of EEG and ML in both understanding brain-based pain mechanisms and using EEG features to differentiate chronic pain subgroups. <h3>References</h3> [1]Fitzcharles MA, Cohen SP, Clauw DJ, Littlejohn G, Usui C, Häuser W. Nociplastic pain: towards an understanding of prevalent pain conditions. Lancet 2021 [2]de Melo GA, Madruga MLLH, Torro N. Electroencephalographic Evaluation in Fibromyalgia: A Systematic Review. Clin EEG Neurosci 2021 <h3>Acknowledgements</h3> Professor Alexander Macgregor for his mentorship and support to the lead author during this work. <h3>Disclosure of Interests</h3> None Declared.